Master Thesis:Battery management system control and estimation design

Lithium-ion batteries have emerged as the primary choice for energy storage solution across a wide range of applications, such as electric vehicles (EVs) and stationary energy storage systems (ESSs). However, as a result of complex interplay of different physical and chemical mechanisms, the performance of lithium-ion batteries degrades as they undergo usage over their lifespan. In some cases, accelerated capacity fade can occur, which results in accelerated performance degradation and even battery safety issues. Therefore, to ensure safe and optimal usage of lithium-ion batteries, non-invasive battery health diagnosis and prognosis (BHDP) is an essential function to be developed in a health-aware battery management system (BMS).
Traditional BHDP approaches are based on periodic sampling (or fixed-time-interval sampling). However, as the ever-increasing availability of sensor data and complexity of algorithms, limited computational resources on the BMS make the deployment of traditional BHDP function very challenging.

Suitable background
  • Must have strong educational background in electrical engineering, engineering physics, mechatronics, or computer science with very good grades in master level courses like nonlinear filtering/estimation, machine learning, deep learning, linear control systems etc.
  • Must have high proficiency in Matlab, Python, and Simulink.
  • You must be self-motivated and meticulous in your problem-solving approach.
  • Familiarity with lithium-ion batteries and some previous experience with machine learning methods will be considered meritorious.
Please send your application including CV, Cover Letter, and Transcript of grades. Application will not be considered without a complete set of the required documents.

Remark: We would prefer if you apply as a group of two students to collaborate and work together on this thesis topic. If this is the case then please submit your applications individually with all the required documents but do write you partner name in your cover letter so that we can evaluate you as a group

Description of thesis work
In this master thesis, our goal is to investigate alternative sampling methods for cost-efficient BHDP function to be deployed on the BMS in the future. Specifically, the main tasks are as follows:
  1. Develop and propose a X sampling-based BHDP framework to improve computational efficiency and BHDP performance.
  2. Collect and preprocess battery usage data and health data.
  3. Validate the effectiveness of the proposed framework and compare its computational efficiency and performance with the traditional framework.

Thesis Title: Comparative Analysis of Sampling Methods for Battery Health Diagnosis and Prognosis.

Thesis Level: Master

Language: English

Starting date: 2024-01-14

Number of students: 2

Huang Zhang
Industrial PhD Student, +46 739022691
ESS Software and Control Design
Department of Electromobility
CampX, Gothenburg, Sweden

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